Hybrid ANN-PSO Based MPPT Optimization for Enhanced Solar Panel Efficiency

In some cases of Solar Power Generation System (PLTS) optimization, AI algorithms can be used to solve complex problems such as efficiency problems. In this research, a hybrid approach that combines Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) algorithms is used to optimize the Maximum Power Point Tracking (MPPT) system for solar panels. The hybrid technique seeks to maximize power output by precisely determining the ideal voltage and current points, which will increase the efficiency of solar panels. This study includes the measurement of parameters such as current (I), voltage (V), and power (W) in the MPPT system. The research shows that the hybrid ANN-PSO approach performs better than the traditional ANN method, producing mean squared error (MSE) and root mean squared error (RMSE) values that are lower. Moreover, research results show that the hybrid system maintains a load efficiency of approximately 51% in real-world measurements and about 67% in simulation data, indicating better performance and implementation ease.